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Review

Application of artificial intelligence techniques in municipal solid waste management: a systematic literature review

ORCID Icon, , , , &
Pages 316-336 | Received 26 Jul 2022, Accepted 19 Mar 2023, Published online: 03 May 2023

References

  • Hoornweg D, Bhada-Tata P. What a waste: a global review of solid waste management. Macrocognition Metrics Scenarios: Design Evaluation Real-World Teams; 2012. p. 29–43. http://hdl.handle.net/10986/17388.
  • Singh D, Satija A. Prediction of municipal solid waste generation for optimum planning and management with artificial neural network — case study: faridabad city in haryana state (India). Int J Sys Assur Eng Manage. 2016: 1–7. doi:10.1007/s13198-016-0484-5.
  • Coban A, Ertis IF, Cavdaroglu NA. Municipal solid waste management via multi-criteria decision making methods: a case study in Istanbul, Turkey. J Cleaner Prod. 2018;180:159–167. doi:10.1016/j.jclepro.2018.01.130.
  • Hannan MA, Arebey M, Begum RA, et al. An automated solid waste bin level detection system using a gray level aura matrix. Waste Manage. 2012;32(12):2229–2238. doi:10.1016/j.wasman.2012.06.002.
  • Ayeleru OO, Fajimi LI, Oboirien BO, et al. Forecasting municipal solid waste quantity using artificial neural network and supported vector machine techniques: a case study of Johannesburg, South Africa. J Cleaner Prod. 2021;289:125671. doi:10.1016/j.jclepro.2020.125671.
  • Cubillos M. Multi-site household waste generation forecasting using a deep learning approach. Waste Manage. 2020;115:8–14. doi:10.1016/j.wasman.2020.06.046.
  • Nguyen XC, Nguyen TTH, La DD, et al. Resources, conservation & recycling development of machine learning - based models to forecast solid waste generation in residential areas: a case study from Vietnam. Res Conserv Recycl. 2021;167(January):105381. doi:10.1016/j.resconrec.2020.105381.
  • Yetilmezsoy K, Ozkaya B, Cakmakci M. Artificial intelligence-based prediction models for environmental engineering. Neural Netw World. 2011;21(3):193–218. doi:10.14311/NNW.2011.21.012.
  • Kalogirou SA. Artificial intelligence for the modeling and control of combustion processes: a review. Prog Energy Combust Sci. 2003;29(6):515–566. doi:10.1016/S0360-1285(03)00058-3.
  • Kitchenham B, Charters S. Guidelines for performing systematic literature reviews in software engineering. IEEE Access. 2007;4:5356–5373. doi:10.1016/j.infsof.2008.09.009.
  • Everitt BS, Skrondal A. The Cambridge dictionary of statistics. 4th ed.. United States of America: Cambridge University Press; 2010.
  • Iqbal M. Statistical comparison Of solar radiation correlations. Sol Energy. 1984;33(2):143–148.
  • Sammut C, Webb GI, editors. Mean absolute error BT - encyclopedia of machine learning. Boston (MA): Springer US; 2010. p. 652. doi:10.1007/978-0-387-30164-8_525.
  • Swamidass PM. MAPE (mean absolute percentage error) BT - encyclopedia of production and manufacturing management. Boston (MA): Springer US; 2000. p. 462. doi:10.1007/1-4020-0612-8_580.
  • Contreras I, Vehi J. Artificial intelligence for diabetes management and decision support: literature review. J Med Internet Res. 2018;20:1–21. doi:10.2196/10775.
  • Rahman MW, Islam R, Hasan A, et al. Intelligent waste management system using deep learning with IoT. J King Saud Univ Comput Inf Sci. 2022;34(5):2072–2087. doi:10.1016/j.jksuci.2020.08.016.
  • Wang C, Qin J, Qu C, et al. A smart municipal waste management system based on deep-learning and internet of things. Waste Manage. 2021;135(August):20–29. doi:10.1016/j.wasman.2021.08.028.
  • Solano Meza JK, Orjuela Yepes D, Rodrigo-Ilarri J, et al. Predictive analysis of urban waste generation for the City of Bogotá, Colombia, through the implementation of decision trees-based machine learning, support vector machines and artificial neural networks. Heliyon. 2019;5(11):e02810. doi:10.1016/j.heliyon.2019.e02810.
  • Dubey S, Singh P, Yadav P, et al. Household waste management system using IoT and machine learning. Procedia Comput Sci. 2020;167(2019):1950–1959. doi:10.1016/j.procs.2020.03.222.
  • Firat M, Erkan M, Ali M. Comparative analysis of neural network techniques for predicting water consumption time series. J Hydrol. 2010;384(1–2):46–51. doi:10.1016/j.jhydrol.2010.01.005.
  • Zhu X, Wu G, Coulon F, et al. Correlating asphaltene dimerization with its molecular structure by potential of mean force calculation and data mining. Energy Fuels. 2018;32(5):5779–5788. doi:10.1021/acs.energyfuels.8b00470.
  • Zhu X, Wang X, Ok YS. The application of machine learning methods for prediction of metal sorption onto biochars. J Hazard Mater. 2019;378. doi:10.1016/j.jhazmat.2019.06.004.
  • Antanasijević D, Pocajt V, Popović I, et al. The forecasting of municipal waste generation using artificial neural networks and sustainability indicators. Sustain Sci. 2013;8(1):37–46. doi:10.1007/s11625-012-0161-9.
  • Sousa V, Dias-ferreira C. Artificial neural network modelling of the amount of separately-collected household packaging waste. J Cleaner Prod. 2019;210:401–409. doi:10.1016/j.jclepro.2018.11.063.
  • Sunayana K, Kumar R. Forecasting of municipal solid waste generation using non-linear autoregressive (NAR) neural models. Waste Manage. 2021;121:206–214. doi:10.1016/j.wasman.2020.12.011.
  • Abbasi M, Abduli MA, Omidvar B, et al. Results uncertainty of support vector machine and hybrid of wavelet transform-support vector machine models for solid waste generation forecasting. Environ Prog Sustain Energy. 2014;33(3):676–680. doi:10.1002/ep.11747.
  • Pourreza Movahed Z, Kabiri M, Ranjbar S, et al. Multi-objective optimization of life cycle assessment of integrated waste management based on genetic algorithms: a case study of Tehran. J Cleaner Prod. 2020;247:119153. doi:10.1016/j.jclepro.2019.119153.
  • Mountrakis G, Im J, Ogole C. Support vector machines in remote sensing: a review. ISPRS J Photogramm Remote Sens. 2011;66(3):247–259. doi:10.1016/j.isprsjprs.2010.11.001.
  • Vapnik VN. The nature of statistical learning theory. New York: Springer-Verlag; 1995.
  • Abbasi M, El Hanandeh A. Forecasting municipal solid waste generation using artificial intelligence modelling approaches. Waste Manage. 2016;56:13–22. doi:10.1016/j.wasman.2016.05.018.
  • Yu P, Chen S, Chang I. Support vector regression for real-time flood stage forecasting. J Hydrol. 2006;328:704–716. doi:10.1016/j.jhydrol.2006.01.021.
  • Noori R, Abdoli M, Ghasrodashti A, et al. Prediction of municipal solid waste generation with combination of support vector machine and principal component analysis: a case study of Mashhad. Environ Prog. 2008;28(2):249–258. doi:10.1002/ep.10317.
  • Jassim MS, Coskuner G, Zontul M. Comparative performance analysis of support vector regression and artificial neural network for prediction of municipal solid waste generation. Waste Manage Res J Sustain Circular Econ. 2022;40(2):195–204. doi:10.1177/0734242X211008526.
  • Adedeji O, Wang Z. Intelligent waste classification system using deep learning convolutional neural network. Procedia Manufact. 2019;35:607–612. doi:10.1016/j.promfg.2019.05.086.
  • Costa BS, Bernardes A, Pereira J, et al.. Artificial intelligence in automated sorting in trash recycling; 2018. p. 198–205. doi:10.5753/eniac.2018.4416.
  • Toğaçar M, Ergen B, Cömert Z. Waste classification using AutoEncoder network with integrated feature selection method in convolutional neural network models. Measurement. 2020;153:107459. doi:10.1016/j.measurement.2019.107459.
  • You H, Ma Z, Tang Y, et al. Comparison of ANN (MLP), ANFIS, SVM, and RF models for the online classification of heating value of burning municipal solid waste in circulating fluidized bed incinerators. Waste Manage. 2017;68:186–197. doi:10.1016/j.wasman.2017.03.044.
  • Abunama T, Othman F. Leachate generation rate modeling using artificial intelligence algorithms aided by input optimization method for an MSW landfill. Environ Sci Pollut Res. 2018a;26:3368–3381.
  • Xue W, Hu X, Wei Z, et al. A fast and easy method for predicting agricultural waste compost maturity by image-based deep learning. Bioresour Technol. 2019;290(May):121761. doi:10.1016/j.biortech.2019.121761.
  • Kannangara M, Dua R, Ahmadi L, et al. Modeling and prediction of regional municipal solid waste generation and diversion in Canada using machine learning approaches. Waste Manage. 2018;74:3–15. doi:10.1016/j.wasman.2017.11.057.
  • Tayefi M, Esmaeili H, Saberi Karimian M, et al. The application of a decision tree to establish the parameters associated with hypertension. Comput Methods Programs Biomed. 2017;139:83–91. doi:10.1016/j.cmpb.2016.10.020.
  • Heshmati RAA, Mokhtari M, Shakiba Rad S. Prediction of the compression ratio for municipal solid waste using decision tree. Waste Manage Res. 2014;32(1):64–69. doi:10.1177/0734242X13512716.
  • Noori R, Abdoli MA, Farokhnia A, et al. Results uncertainty of solid waste generation forecasting by hybrid of wavelet transform-ANFIS and wavelet transform-neural network. Expert Syst Appl. 2009;36(6):9991–9999. doi:10.1016/j.eswa.2008.12.035.
  • Turhan C, Simani S, Zajic I, et al. Performance analysis of data-driven and model-based control strategies applied to a thermal unit model. Energies. 2017;10(1):1–20. doi:10.3390/en10010067.
  • Abbasi M, Rastgoo MN, Nakisa B. Monthly and seasonal modeling of municipal waste generation using radial basis function neural network. Environ Prog Sustain Energy. 2019;38(3):1–10. doi:10.1002/ep.13033.
  • Abunama T, Othman F. Predicting sanitary landfill leachate generation in humid regions using ANFIS modeling. Environ Monit Assess. 2018;190(2018):15–11.
  • Abdallah M, Warith M, Narbaitz R, et al. Combining fuzzy logic and neural networks in modeling landfill gas production. World Acad Sci Eng Technol. 2011;78:559–565.
  • Bandara NJGJ, Wirasinghe SC, Pilapiiya S. Relation of waste generation and composition to socio-economic factors: a case study. Environ Monit Assess. 2007;135:31–39. doi:10.1007/s10661-007-9705-3.
  • Afroz R, Hanaki K, Tudin R. Factors affecting waste generation: a study in a waste management program in Dhaka city, Bangladesh. Environ Monit Assess. 2011;179:509–519. doi:10.1007/s10661-010-1753-4.
  • Expósito-márquez A, Expósito-Izquierdo C, Brito-Santana J, et al. Computers & industrial engineering greedy randomized adaptive search procedure to design waste collection routes in La palma. Comput Ind Eng. 2019;137(August):106047. doi:10.1016/j.cie.2019.106047.
  • Younes MK, Nopiah ZM, Basri NEA, et al. Prediction of municipal solid waste generation using nonlinear autoregressive network. Environ Monit Assess. 2015;187(12):1–10. doi:10.1007/s10661-015-4977-5.
  • Wu H, Tao F, Yang B. Optimization of vehicle routing for waste collection and transportation. Int J Environ Res Public Health. 2020;17(14):49–63. doi:10.3390/ijerph17144963.
  • Ali SA, Ahmad A. Forecasting MSW generation using artificial neural network time series model: a study from metropolitan city. SN Appl Sci. 2019;1(11):1–16. doi:10.1007/s42452-019-1382-7.
  • Azadi S, Karimi-Jashni A. Verifying the performance of artificial neural network and multiple linear regression in predicting the mean seasonal municipal solid waste generation rate: a case study of fars province, Iran. Waste Manage. 2016;48:14–23. doi:10.1016/j.wasman.2015.09.034.
  • Jalili Ghazi Zade M, Noori R. Prediction of municipal solid waste generation by use of artificial neural network: a case study of mashhad. Int J Environ Res. 2008;2(1):13–22.
  • Zhang Q, Yang Q, Bao Q, et al. Waste image classification based on transfer learning and convolutional neural network. Waste Manage. 2021;135(May):150–157. doi:10.1016/j.wasman.2021.08.038.
  • Vo AH, Hoang Son L, Le T. A novel framework for trash classification using deep transfer learning. IEEE Access. 2019;7:178631–178639. doi:10.1109/ACCESS.2019.2959033.
  • Liang S, Gu Y. A deep convolutional neural network to simultaneously localize and recognize waste types in images. Waste Manage. 2021;126:247–257. doi:10.1016/j.wasman.2021.03.017.
  • Mao W, Chen W-C, Wang C-T, et al. Recycling waste classification using optimized convolutional neural network. Resour Conserv Recycl. 2021;164(August 2020):105132. doi:10.1016/j.resconrec.2020.105132.
  • Chu Y, Huang C, Xie X, et al. Multilayer hybrid deep-learning method for waste classification and recycling. Comput Intell Neurosci. 2018;2018:1–9. doi:10.1155/2018/5060857.
  • Yang M, Thung G. Classification of trash for recyclability status. CS229Project Report, 2016. p. 1–6.
  • Bircanoglu C, Atay MS, Beser F, et al.. RecycleNet: Intelligent Waste Sorting Using Deep Neural Networks. 2018. doi:10.1109/INISTA.2018.8466276.
  • Babaee Tirkolaee E, Abbasian P, Soltani M, et al. Developing an applied algorithm for multi-trip vehicle routing problem with time windows in urban waste collection: a case study. Waste Manage Res. 2019;37(1_suppl):4–13. doi:10.1177/0734242X18807001.
  • Tirkolaee EB, Alinaghian M, Hosseinabadi AAR, et al. An improved ant colony optimization for the multi-trip capacitated Arc routing problem. Comput Electr Eng. 2019;77:457–470. doi:10.1016/j.compeleceng.2018.01.040.
  • Abdallah M, Adghim M, Maraqa M, et al. Simulation and optimization of dynamic waste collection routes. Waste Manage Res. 2019;37(8):793–802. doi:10.1177/0734242X19833152.
  • Amal L, Son LH, Chabchoub H. SGA: spatial GIS-based genetic algorithm for route optimization of municipal solid waste collection. Environ Sci Pollut Res. 2018;25(27):27569–27582. doi:10.1007/s11356-018-2826-0.
  • Vu HL, Bolingbroke D, Fallah B. Assessment of waste characteristics and their impact on GIS vehicle collection route optimization using ANN waste forecasts. Waste Manage. 2019;88:118–130. doi:10.1016/j.wasman.2019.03.037.
  • Hintsch T, Irnich S. Large multiple neighborhood search for the clustered vehicle-routing problem. Eur J Oper Res. 2018;270(1):118–131. doi:10.1016/j.ejor.2018.02.056.
  • Al-refaie A, Al-hawadi A, Fraij S. Optimization models for clustering of solid waste collection process. Eng Optim. 2021;53(12):2056–2069. doi:10.1080/0305215X.2020.1843165.
  • Ramos TRP, de Morais CS, Barbosa-Póvoa AP. The smart waste collection routing problem: alternative operational management approaches. Expert Syst Appl. 2018;103:146–158. doi:10.1016/j.eswa.2018.03.001.
  • Hannan MA, Arebey M, Begum RA, et al. An automated solid waste bin level detection system using gabor wavelet filters and multi-layer perception. Resour Conserv Recycl. 2013;72(2013):33–42. doi:10.1016/j.resconrec.2012.12.002.
  • Rutqvist D, Kleyko D, Blomstedt F. An automated machine learning approach for smart waste management systems. IEEE Trans Ind Inf. 2020;16(1):384–392. doi:10.1109/TII.2019.2915572.
  • Islam S, Hannan MA, Basri H, et al. Solid waste bin detection and classification using dynamic time warping and MLP classifier. Waste Manage. 2014;34(2):281–290. doi:10.1016/j.wasman.2013.10.030.
  • Wainaina S, Awasthi MK, Sarsaiya S, et al. Bioresource technology resource recovery and circular economy from organic solid waste using aerobic and anaerobic digestion technologies. Bioresour Technol. 2020;301(January):122778. doi:10.1016/j.biortech.2020.122778.
  • Arvanitoyannis IS. Waste management for polymers in food packaging industries. first edit, plastic films in food packaging: materials, technology and applications. 1st ed. Elsevier; 2012. doi:10.1016/B978-1-4557-3112-1.00014-4.
  • Lakshmikanthan P. Value creation with waste to energy: economic considerations, current developments in biotechnology and bioengineering: waste treatment processes for energy generation. Elsevier B.V; 2019. doi:10.1016/B978-0-444-64083-3.00015-4.
  • Boniecki P, Dach J, Pilarski K. Arti fi cial neural networks for modeling ammonia emissions released from sewage sludge composting. Atmos Environ. 2012;57:49–54. doi:10.1016/j.atmosenv.2012.04.036.
  • Kujawa S, Mazurkiewicz J, Czeka W. Using convolutional neural networks to classify the maturity of compost based on sewage sludge and rapeseed straw. J Cleaner Prod. 2020;258:120814. doi:10.1016/j.jclepro.2020.120814.
  • Drudi KCR, Martins G, Antonio GC, et al. Statistical model for heating value of municipal solid waste in Brazil based on gravimetric composition. Waste Manage. 2019;87:782–790. doi:10.1016/j.wasman.2019.03.012.
  • Shu HY, Lu H-C, Fan H-J, et al. Prediction for energy content of Taiwan municipal solid waste using multilayer perceptron neural networks. J Air Waste Manage Assoc. 2006;56(6):852–858. doi:10.1080/10473289.2006.10464497.
  • Olatunji OO, Akinlabi S, Madushele N, et al. Multilayer perceptron artificial neural network for the prediction of heating value of municipal solid waste. AIMS Energy. 2019;7(6):944–956. doi:10.3934/energy.2019.6.944.
  • Birgen C, Magnanelli E, Carlsson P, et al. Machine learning based modelling for lower heating value prediction of municipal solid waste. Fuel. 2021;283(August 2020):118906. doi:10.1016/j.fuel.2020.118906.
  • Rostami A, Baghban A. Application of a supervised learning machine for accurate prognostication of higher heating values of solid wastes. Energ Source Part A Recovery Utilizat Environ Effects. 2018;40(5):558–564. doi:10.1080/15567036.2017.1360967.
  • Fallah B, Ng KTW, Vu HL, et al. Application of a multi-stage neural network approach for time-series landfill gas modeling with missing data imputation. Waste Manage. 2020;116:66–78. doi:10.1016/j.wasman.2020.07.034.
  • Hoque MM, Rahman MTU. Landfill area estimation based on solid waste collection prediction using ANN model and final waste disposal options. J Cleaner Prod. 2020;256:120387. doi:10.1016/j.jclepro.2020.120387.
  • Younes MK, Nopiah ZM, Basri NEA, et al. Landfill area estimation based on integrated waste disposal options and solid waste forecasting using modified ANFIS model. Waste Manage. 2016;55:3–11. doi:10.1016/j.wasman.2015.10.020.
  • Wieland D, Wotawa F, Wotawa G. From neural networks to qualitative models in environmental engineering. Comput-Aided Civ Inf. 2002;17:104–118.

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